Fast-PGM: Fast Probabilistic Graphical Model Learning and Inference

TL;DR

Fast-PGM is an open-source library that significantly improves the efficiency and usability of probabilistic graphical models by supporting comprehensive learning and inference tasks with advanced optimizations and user-friendly features.

Contribution

The paper introduces Fast-PGM, a novel library that enhances PGM learning and inference efficiency while improving usability through flexible interfaces and detailed documentation.

Findings

Fast-PGM achieves faster inference times compared to existing tools.

The library supports both exact and approximate inference methods.

Fast-PGM is accessible to users with varying levels of expertise.

Abstract

Probabilistic graphical models (PGMs) serve as a powerful framework for modeling complex systems with uncertainty and extracting valuable insights from data. However, users face challenges when applying PGMs to their problems in terms of efficiency and usability. This paper presents Fast-PGM, an efficient and open-source library for PGM learning and inference. Fast-PGM supports comprehensive tasks on PGMs, including structure and parameter learning, as well as exact and approximate inference, and enhances efficiency of the tasks through computational and memory optimizations and parallelization techniques. Concurrently, Fast-PGM furnishes developers with flexible building blocks, furnishes learners with detailed documentation, and affords non-experts user-friendly interfaces, thereby ameliorating the usability of PGMs to users across a spectrum of expertise levels. The source code of Fast-PGM is available at https://github.com/jjiantong/FastPGM.